Diffusion models have established themselves as state-of-the-art generative models across various data modalities, including images and videos, due to their ability to accurately approximate complex data distributions. Unlike traditional generative approaches such as VAEs and GANs, diffusion models employ a progressive denoising process that transforms noise into meaningful data over multiple iterative steps. This gradual approach enhances their expressiveness and generation quality. Not only that, diffusion models have also been shown to extract meaningful representations from data while learning to generate samples. Despite their success, the application of diffusion models to graph-structured data remains relatively unexplored, primarily due to the discrete nature of graphs, which necessitates discrete diffusion processes distinct from the continuous methods used in other domains. In this work, we leverage the representational capabilities of diffusion models to learn meaningful embeddings for graph data. By training a discrete diffusion model within an autoencoder framework, we enable both effective autoencoding and representation learning tailored to the unique characteristics of graph-structured data. We extract the representation from the combination of the encoder's output and the decoder's first time step hidden embedding. Our approach demonstrates the potential of discrete diffusion models to be used for graph representation learning. The code can be found at https://github.com/DanielMitiku/Graph-Representation-Learning-with-Diffusion-Generative-Models

翻译：扩散模型因其能够精确逼近复杂数据分布的能力，已在包括图像和视频在内的多种数据模态上确立了其作为最先进生成模型的地位。与变分自编码器（VAEs）和生成对抗网络（GANs）等传统生成方法不同，扩散模型采用渐进式去噪过程，通过多个迭代步骤将噪声转化为有意义的数据。这种渐进式方法增强了其表达能力和生成质量。不仅如此，扩散模型还被证明能在学习生成样本的同时，从数据中提取有意义的表示。尽管取得了这些成功，扩散模型在图结构数据上的应用仍相对未被充分探索，这主要是由于图的离散性质，这需要不同于其他领域所用连续方法的离散扩散过程。在本工作中，我们利用扩散模型的表示能力来学习图数据的有意义嵌入。通过在自编码器框架内训练一个离散扩散模型，我们实现了针对图结构数据独特特性的有效自编码和表示学习。我们从编码器输出与解码器第一个时间步的隐藏嵌入的组合中提取表示。我们的方法证明了离散扩散模型用于图表示学习的潜力。代码可在 https://github.com/DanielMitiku/Graph-Representation-Learning-with-Diffusion-Generative-Models 找到。